mcpbeat

Call Chain

athola/call-chain

Traces execution paths through the code graph with criticality scoring and Mermaid charts. Use when understanding how a function propagates through the system.

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on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/athola/claude-night-market --skill call-chain

The instruction itself

6 sections, as written by the author

Call Chain Tracing

Trace execution flows through the codebase using the

code knowledge graph.

When NOT To Use

  • Static import relationships (use cartograph:dependency-graph)
  • Scoring the risk of a change (use pensive:blast-radius)

Prerequisites

This skill requires the gauntlet plugin for graph

data. Discover it:

GRAPH_QUERY=$(find ~/.claude/plugins -name "graph_query.py" -path "*/gauntlet/*" 2>/dev/null | head -1)

If gauntlet is not installed: Fall back to static

analysis. Use grep to trace function calls and build

a Mermaid diagram manually from import/call patterns.

Skip graph-specific steps.

If installed but no graph.db: Tell the user to run

/gauntlet-graph build.

Steps

  • Accept target: Get a function name or entry point

from the user (or trace all entry points).

  • Run flow tracing (requires gauntlet):
   python3 "$GRAPH_QUERY" --action flows --depth 15

To filter by entry point:

   python3 "$GRAPH_QUERY" --action flows --entry "main"

Fallback (no gauntlet): Trace calls with rg (or grep):

   # Prefer rg (ripgrep) for speed; fall back to grep
   if command -v rg &>/dev/null; then
     rg -n "function_name\(" --type py . | head -20
   else
     grep -rn "function_name(" --include="*.py" . | head -20
   fi

Build the call tree manually from search results.

  • Display as indented tree:
   main() [criticality: 0.72]
     -> validate_input()
       -> parse_config()
     -> process_data()
       -> db.execute_query()
       -> cache.store()
     -> send_response()
  • Generate Mermaid flowchart:
   flowchart LR
     main --> validate_input
     main --> process_data
     main --> send_response
     validate_input --> parse_config
     process_data --> db.execute_query
     process_data --> cache.store
  • Show criticality breakdown:
  • File spread: how many files the flow touches
  • Security sensitivity: auth/crypto code in the path
  • Test coverage gaps: untested nodes in the flow

Criticality Scoring

| Factor | Weight | Meaning |

|--------|--------|---------|

| File spread | 0.30 | Touches many files |

| Security | 0.25 | Contains auth/crypto code |

| External calls | 0.20 | Unresolved dependencies |

| Test gap | 0.15 | Untested nodes in flow |

| Depth | 0.10 | Deep call chains |

Exit Criteria

  • [ ] Indented call tree displayed for the target function with

criticality scores in the form [criticality: N.NN]

  • [ ] Mermaid flowchart LR generated with edges representing

each caller-to-callee relationship in the traced path

  • [ ] Criticality breakdown table shown covering: file spread,

security sensitivity, external calls, test gap, and depth

  • [ ] If gauntlet is not installed, fallback to static rg/grep

analysis is used and the absence of graph data is noted

  • [ ] If gauntlet is installed but graph.db is absent, user is

told to run /gauntlet-graph build before the skill halts

How to use it

Copy the folder

Take athola/call-chain from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.